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4e62a86
1
Parent(s):
7a692a6
why aren't you working
Browse files
web2json/__pycache__/ai_extractor.cpython-311.pyc
CHANGED
Binary files a/web2json/__pycache__/ai_extractor.cpython-311.pyc and b/web2json/__pycache__/ai_extractor.cpython-311.pyc differ
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web2json/ai_extractor.py
CHANGED
@@ -224,7 +224,7 @@ class NvidiaLLMClient(LLMClient):
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"""
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self.model_name = model_name
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-
@retry_on_ratelimit(max_retries=
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def call_api(self, prompt: str) -> str:
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"""
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Call the NVIDIA API with the given prompt (non-streaming).
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@@ -241,7 +241,8 @@ class NvidiaLLMClient(LLMClient):
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messages=[{"role": "user", "content": prompt}],
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temperature=self.temperature,
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top_p=self.top_p,
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-
max_tokens=self.max_tokens
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# stream is omitted (defaults to False)
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)
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# print("DONE")
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@@ -262,6 +263,7 @@ class NvidiaLLMClient(LLMClient):
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idx = futures[fut]
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try:
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results[idx] = fut.result()
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except RateLimitError:
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# You could set results[idx] = None or a default string
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results[idx] = f"<failed after retries>"
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@@ -367,6 +369,7 @@ class ModalRerankerClient(RerankerClient):
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scores = np.array([p["score"] for p in ranked], dtype=float)
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# Max normalization
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max_score = scores.max() if len(scores) > 0 else 1.0
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if max_score == 0:
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norm_scores = np.zeros_like(scores)
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else:
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@@ -381,6 +384,7 @@ class ModalRerankerClient(RerankerClient):
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for p, norm in filtered
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]
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return docs
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"""
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self.model_name = model_name
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@retry_on_ratelimit(max_retries=20, base_delay=0.5, max_delay=5.0)
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def call_api(self, prompt: str) -> str:
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"""
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Call the NVIDIA API with the given prompt (non-streaming).
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messages=[{"role": "user", "content": prompt}],
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temperature=self.temperature,
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top_p=self.top_p,
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max_tokens=self.max_tokens,
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extra_body={"chat_template_kwargs": {"thinking":True}},
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# stream is omitted (defaults to False)
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)
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# print("DONE")
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idx = futures[fut]
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try:
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results[idx] = fut.result()
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print("DONE")
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except RateLimitError:
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# You could set results[idx] = None or a default string
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results[idx] = f"<failed after retries>"
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scores = np.array([p["score"] for p in ranked], dtype=float)
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# Max normalization
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max_score = scores.max() if len(scores) > 0 else 1.0
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# max_score = 1
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if max_score == 0:
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norm_scores = np.zeros_like(scores)
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else:
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for p, norm in filtered
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]
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# docs.reverse()
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return docs
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